Leveraging AI and early structural insights to power rational antibody lead design and optimization
28 Apr 2026
Lead Identification & Optimization
- How can we obtain sufficient high-quality antibody–antigen structural and binding data to prevent sparse or low-resolution datasets from limiting AI model accuracy?
- In what ways can AI effectively balance multi-parameter optimization—affinity, developability, stability, immunogenicity, and manufacturability—rather than optimizing any single property in isolation?
- How can we improve the accuracy of structural predictions for flexible CDR loops and antibody–antigen interfaces, especially where induced-fit effects make affinity and epitope prediction difficult?
- What strategies can overcome the experimental validation bottleneck, enabling faster wet-lab testing and smarter active-learning–driven selection of antibody sequences for synthesis?
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